cuml/ridge_regression_demo.ipynb

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   "source": [
    "# Ridge Regression Demo\n",
    "Ridge extends linear regression by providing L2 regularization of the coefficients. It can reduce the variance of the predictors, and improves the conditioning of the problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model can take array-like objects, either in host as NumPy arrays or in device (as Numba or cuda_array_interface-compliant), as well as cuDF DataFrames as the input. \n",
    "\n",
    "For information about cuDF, refer to the cuDF documentation: https://rapidsai.github.io/projects/cudf/en/latest/\n",
    "\n",
    "For information about cuML's ridge regression implementation: https://rapidsai.github.io/projects/cuml/en/latest/api.html#ridge-regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import pandas as pd\n",
    "import cudf as gd\n",
    "\n",
    "from sklearn.datasets import make_regression\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "from cuml.linear_model import Ridge as cuRidge\n",
    "from sklearn.linear_model import Ridge as skRidge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_samples = 2**20\n",
    "n_features = 399"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Data\n",
    "\n",
    "### Host"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "X,y = make_regression(n_samples=n_samples, n_features=n_features, random_state=0)\n",
    "\n",
    "X = pd.DataFrame(X)\n",
    "y = pd.DataFrame(y)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "X_cudf = gd.DataFrame.from_pandas(X_train)\n",
    "X_cudf_test = gd.DataFrame.from_pandas(X_test)\n",
    "\n",
    "y_cudf = gd.Series(y_train.values[:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-learn Model\n",
    "\n",
    "### Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "ridge_sk = skRidge(fit_intercept=False,\n",
    "                   normalize=True,\n",
    "                   alpha=0.1)\n",
    "\n",
    "ridge_sk.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "predict_sk= ridge_sk.predict(X_test)\n",
    "error_sk = mean_squared_error(y_test, predict_sk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cuML Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# run the cuml ridge regression model to fit the training dataset.  Eig is the faster algorithm, but svd is more accurate \n",
    "ridge_cuml = cuRidge(fit_intercept=False,\n",
    "                     normalize=True,\n",
    "                     solver='svd',\n",
    "                     alpha=0.1)\n",
    "\n",
    "ridge_cuml.fit(X_cudf, y_cudf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "predict_cuml = ridge_cuml.predict(X_cudf_test).to_array()\n",
    "error_cuml = mean_squared_error(y_test, predict_cuml)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"SKL MSE(y): %s\" % error_sk)\n",
    "print(\"CUML MSE(y): %s\" % error_cuml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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